Some Additional Distributions

Provides the density, distribution, quantile and generation functions of some obscure probability distributions, including the doubly non- central t, F, Beta, and Eta distributions; the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares; the product of non-central chi-squares to powers; the product of doubly non-central F variables; the product of independent normals.


Some Additional Distributions apparently not available in R.

-- Steven E. Pav, shabbychef@gmail.com

This package can be installed from CRAN, via drat, or from github:

# via CRAN:
install.packages("sadists")
# via drat:
if (require(drat)) {
    drat:::add("shabbychef")
    install.packages("sadists")
}
# via devtools (typically 'master' is stable):
if (require(devtools)) {
    install_github("shabbychef/sadists")
}

First some functions to test the 'dpqr' functions:

testf <- function(dpqr, nobs, ...) {
    require(ggplot2)
    require(grid)
    
    set.seed(3940071)
    rv <- sort(dpqr$r(nobs, ...))
    data <- data.frame(draws = rv, pvals = dpqr$p(rv, 
        ...))
    text.size <- 8  # sigh
    
    # http://stackoverflow.com/a/5688125/164611
    p1 <- qplot(rv, geom = "blank") + geom_line(aes(y = ..density.., 
        colour = "Empirical"), stat = "density") + 
        stat_function(fun = function(x) {
            dpqr$d(x, ...)
        }, aes(colour = "Theoretical")) + geom_histogram(aes(y = ..density..), 
        alpha = 0.3) + scale_colour_manual(name = "Density", 
        values = c("red", "blue")) + theme(text = element_text(size = text.size)) + 
        labs(title = "Density (tests dfunc)")
    
    # Q-Q plot
    p2 <- ggplot(data, aes(sample = draws)) + stat_qq(distribution = function(p) {
        dpqr$q(p, ...)
    }) + geom_abline(slope = 1, intercept = 0, colour = "red") + 
        theme(text = element_text(size = text.size)) + 
        labs(title = "Q-Q plot (tests qfunc)")
    
    # empirical CDF of the p-values; should be uniform
    p3 <- ggplot(data, aes(sample = pvals)) + stat_qq(distribution = qunif) + 
        geom_abline(slope = 1, intercept = 0, colour = "red") + 
        theme(text = element_text(size = text.size)) + 
        labs(title = "P-P plot (tests pfunc)")
    
    # Define grid layout to locate plots and print each
    # graph
    pushViewport(viewport(layout = grid.layout(2, 2)))
    print(p1, vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
    print(p2, vp = viewport(layout.pos.row = 2, layout.pos.col = 1))
    print(p3, vp = viewport(layout.pos.row = 2, layout.pos.col = 2))
}

This distribution is the weighted sum of independent non-central chi-square variates taken to some powers. The special case where the powers are all one half is related to the upsilon distribution. The special case where the powers are all one could be used to compute the distribution of the (doubly non-central) F distribution.

require(sadists)
wts <- c(-1, 1, 3, -3)
df <- c(100, 200, 100, 50)
ncp <- c(0, 1, 0.5, 2)
pow <- c(1, 0.5, 2, 1.5)
testf(list(d = dsumchisqpow, p = psumchisqpow, q = qsumchisqpow, 
    r = rsumchisqpow), nobs = 2^14, wts, df, ncp, pow)

The K-prime distribution is the weighted sum of a standard normal and an independent central chi, all divided by another independent central chi. Depending on the degrees of freedom and the weights, the K-prime can appears as a Lambda-prime, a normal, or a central t.

require(sadists)
v1 <- 50
v2 <- 80
a <- 0.5
b <- 1.5
testf(list(d = dkprime, p = pkprime, q = qkprime, r = rkprime), 
    nobs = 2^14, v1, v2, a, b)

A Lambda prime random variable is the sum of a standard normal and an independent, scaled central chi random variable.

require(sadists)
df <- 70
ts <- 2
testf(list(d = dlambdap, p = plambdap, q = qlambdap, 
    r = rlambdap), nobs = 2^14, df, ts)

An upsilon random variable is the sum of a standard normal and the weighted sum of several indpendent central chis.

require(sadists)
df <- c(30, 50, 100, 20, 10)
ts <- c(-3, 2, 5, -4, 1)
testf(list(d = dupsilon, p = pupsilon, q = qupsilon, 
    r = rupsilon), nobs = 2^14, df, ts)

The doubly non-central t distribution generalizes the t distribution to the case where the denominator chi-square is non-central.

require(sadists)
df <- 75
ncp1 <- 2
ncp2 <- 3
testf(list(d = ddnt, p = pdnt, q = qdnt, r = rdnt), 
    nobs = 2^14, df, ncp1, ncp2)

The doubly non-central F distribution generalizes the F distribution to the case where the denominator chi-square is non-central.

require(sadists)
df1 <- 40
df2 <- 80
ncp1 <- 1.5
ncp2 <- 2.5
testf(list(d = ddnf, p = pdnf, q = qdnf, r = rdnf), 
    nobs = 2^14, df1, df2, ncp1, ncp2)

The doubly non-central Beta distribution can be viewed as a transformation of the doubly non-central F distribution.

require(sadists)
df1 <- 40
df2 <- 80
ncp1 <- 1.5
ncp2 <- 2.5
testf(list(d = ddnbeta, p = pdnbeta, q = qdnbeta, r = rdnbeta), 
    nobs = 2^14, df1, df2, ncp1, ncp2)

The doubly non-central Eta distribution can be viewed as the square root of the doubly non-central Beta distribution. It is a transform of the doubly non-central t distribution.

require(sadists)
df <- 100
ncp1 <- 0.5
ncp2 <- 2.5
testf(list(d = ddneta, p = pdneta, q = qdneta, r = rdneta), 
    nobs = 2^14, df, ncp1, ncp2)

This distribution is the sum of logs of independent non-central chi-square variates.

require(sadists)
wts <- c(5, -4, 10, -15)
df <- c(100, 200, 100, 50)
ncp <- c(0, 1, 0.5, 2)
testf(list(d = dsumlogchisq, p = psumlogchisq, q = qsumlogchisq, 
    r = rsumlogchisq), nobs = 2^14, wts, df, ncp)

This distribution is the product of independent doubly non-central F variates. The PDQ functions are computed by transformation on the sum of log chi-squares distribution.

require(sadists)
df1 <- c(10, 20, 5)
df2 <- c(1000, 500, 150)
ncp1 <- c(1, 0, 2.5)
ncp2 <- c(0, 1.5, 5)
testf(list(d = dproddnf, p = pproddnf, q = qproddnf, 
    r = rproddnf), nobs = 2^14, df1, df2, ncp1, ncp2)

This distribution is the product of independent non-central chi-square variates taken to some powers. The PDQ functions are computed by transformation on the sum of log chi-squares distribution.

require(sadists)
df <- c(100, 200, 100, 50)
ncp <- c(0, 1, 0.5, 2)
pow <- c(1, 0.5, 2, 1.5)
testf(list(d = dprodchisqpow, p = pprodchisqpow, q = qprodchisqpow, 
    r = rprodchisqpow), nobs = 2^14, df, ncp, pow)

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Reference manual

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install.packages("sadists")

0.2.3 by Steven E. Pav, 8 months ago


https://github.com/shabbychef/sadists


Report a bug at https://github.com/shabbychef/sadists/issues


Browse source code at https://github.com/cran/sadists


Authors: Steven E. Pav [aut, cre]


Documentation:   PDF Manual  


Task views: Probability Distributions


LGPL-3 license


Imports PDQutils, hypergeo, orthopolynom

Suggests shiny, testthat, ggplot2, xtable, knitr


Imported by SharpeR.


See at CRAN